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. 2023 Jun 15:17:1171417.
doi: 10.3389/fnins.2023.1171417. eCollection 2023.

Eye movement changes as an indicator of mild cognitive impairment

Julius Opwonya  1   2 , Boncho Ku  1 , Kun Ho Lee  3   4   5 , Joong Il Kim  1 , Jaeuk U Kim  1   2
Affiliations

Eye movement changes as an indicator of mild cognitive impairment

Julius Opwonya et al. Front Neurosci. .

Erratum in

Abstract

Background: Early identification of patients at risk of dementia, alongside timely medical intervention, can prevent disease progression. Despite their potential clinical utility, the application of diagnostic tools, such as neuropsychological assessments and neuroimaging biomarkers, is hindered by their high cost and time-consuming administration, rendering them impractical for widespread implementation in the general population. We aimed to develop non-invasive and cost-effective classification models for predicting mild cognitive impairment (MCI) using eye movement (EM) data.

Methods: We collected eye-tracking (ET) data from 594 subjects, 428 cognitively normal controls, and 166 patients with MCI while they performed prosaccade/antisaccade and go/no-go tasks. Logistic regression (LR) was used to calculate the EM metrics' odds ratios (ORs). We then used machine learning models to construct classification models using EM metrics, demographic characteristics, and brief cognitive screening test scores. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUROC).

Results: LR models revealed that several EM metrics are significantly associated with increased odds of MCI, with odds ratios ranging from 1.213 to 1.621. The AUROC scores for models utilizing demographic information and either EM metrics or MMSE were 0.752 and 0.767, respectively. Combining all features, including demographic, MMSE, and EM, notably resulted in the best-performing model, which achieved an AUROC of 0.840.

Conclusion: Changes in EM metrics linked with MCI are associated with attentional and executive function deficits. EM metrics combined with demographics and cognitive test scores enhance MCI prediction, making it a non-invasive, cost-effective method to identify early stages of cognitive decline.

Keywords: Alzheimer’s disease; eye movement analysis and synthesis; machine learning (ML); mild cognitive impairment; saccades.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
PS/AS and Go/No-go paradigms.
Figure 2
Figure 2
ROC curves for the best-performing prediction models per feature set.
Figure 3
Figure 3
The top 5 features that contributed the most to the best-performing model in each dataset. (A) RF model with the EM dataset, (B) RF model with demographic and EM data, and (C) XGB model with demographic data, MMSE scores, and EM data. Important features for the LR and SVM models were selected according to decreasing importance to the AUROC value when the feature was permuted.
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